J
Jan Ihmels
Researcher at Weizmann Institute of Science
Publications - 15
Citations - 5657
Jan Ihmels is an academic researcher from Weizmann Institute of Science. The author has contributed to research in topics: Gene & Regulation of gene expression. The author has an hindex of 13, co-authored 15 publications receiving 5454 citations. Previous affiliations of Jan Ihmels include University of California, San Francisco & Howard Hughes Medical Institute.
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Journal ArticleDOI
Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise
John R. S. Newman,Sina Ghaemmaghami,Sina Ghaemmaghami,Jan Ihmels,David K. Breslow,Matthew Noble,Joseph L. DeRisi,Joseph L. DeRisi,Jonathan S. Weissman +8 more
TL;DR: A strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution is presented, revealing a remarkable structure to biological noise.
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Exploration of the Function and Organization of the Yeast Early Secretory Pathway through an Epistatic Miniarray Profile
Maya Schuldiner,Sean R. Collins,Natalie J. Thompson,Vladimir Denic,Arunashree Bhamidipati,Thanuja Punna,Jan Ihmels,Brenda J. Andrews,Charles Boone,Jack Greenblatt,Jonathan S. Weissman,Nevan J. Krogan +11 more
TL;DR: Analysis of an E-MAP of genes acting in the yeast early secretory pathway revealed or clarified the role of many proteins involved in extensively studied processes such as sphingolipid metabolism and retention of HDEL proteins.
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Revealing modular organization in the yeast transcriptional network
TL;DR: The approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods, and uses the method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements.
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Iterative signature algorithm for the analysis of large-scale gene expression data.
TL;DR: It is shown analytically that for noisy expression data the proposed approach leads to better classification due to the implementation of the threshold, and argues that the method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied.
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Similarities and Differences in Genome-Wide Expression Data of Six Organisms
TL;DR: A comparative study of large datasets of expression profiles from six evolutionarily distant organisms finds that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular.